Internal wave detection and location in SAR images using wavelet transform

Abstract

An efficient computational framework far the extraction of mesoscale features, i.e., internal waves, present in synthetic aperture radar (SAR) images is discussed. The manifestation of internal wave packets on SAR ocean images has always been of considerable interest to oceanographers. If, as seems likely, the gray tone patterns of these images can be confirmed to correspond to trough and crest patterns of internal waves, a great deal can be learned about internal waves from satellite data. The first problem in a mesoscale detection system is to distinguish sea from land in the SAR imagery. A method for coastline detection based on a sequence of basic processing procedures followed by a contour tracing algorithm is introduced to obtain sea-land separation to enhance the internal wave detection problem. The utility of wavelet analysis as a tool for automatic oceanic internal wave detection and orientation from SAR images is then examined using the two-dimensional (2D) wavelet transform based on the multiscale gradient detection method. The authors show that the evolution of local maxima of the wavelet transform across scales characterize the local shape of these quasilinear periodic structures. The results from this study, on several ERS-1 and RADARSAT SAR images, show that wavelet analysis is an excellent tool for detecting and locating internal wave features from satellite images against internal wave look-alikes.